Image denoising based on gaussian/bilateral filter and its method noise thresholding

The Gaussian filter is a local and linear filter that smoothes the whole image irrespective of its edges or details, whereas the bilateral filter is also a local but non-linear, considers both gray level similarities and geometric closeness of the neighboring pixels without smoothing edges. The extension of bilateral filter: multi-resolution bilateral filter, where bilateral filter is applied to approximation subbands of an image decomposed and after each level of wavelet reconstruction. The application of bilateral filter on the approximation subband results in loss of some image details, whereas that after each level of wavelet reconstruction flattens the gray levels thereby resulting in a cartoon-like appearance. To tackle these issues, it is proposed to use the blend of Gaussian/bilateral filter and its method noise thresholding using wavelets. In Gaussian noise scenarios, the performance of proposed methods is compared with existing denoising methods and found that, it has inferior performance compared to Bayesian least squares estimate using Gaussian Scale mixture and superior/comparable performance to that of wavelet thresholding, bilateral filter, multi-resolution bilateral filter, NL-means and Kernel based methods. Further, proposed methods have the advantage of less computational time compared to other methods except wavelet thresholding, bilateral filter.     

红外图像改进非局部均值滤波算法研究

为了有效地滤除红外图像中的噪声,提出了一种改进型非局部均值滤波算法(INLMF)。该算法首先针对传统算法中采用固定尺寸的方形图像块无法有效刻画图像中大量分布的细节信息这一缺陷,结合图像中像素点灰度信息提出了一种图像块自适应划分方法,使得划分后的图像块在尺寸和形状上依赖于图像中灰度信息的实际分布情况;其次引入结构相似度因子对算法中的图像块权重值计算方法进行适当改进;最后分别将INLMF算法与已有的两类改进型NLMF算法对两幅红外监控图像进行滤波,并进行了理论分析和实验验证。结果表明,INLMF算法相对于其余几类算法而言,滤波效果较好,该研究对于提高红外图像滤波效果是有帮助的。     

Iterative non-local means filter for salt and pepper noise removal

Salt and Pepper noise (S&P noise) removal is an active research area in digital image processing. Existing techniques commonly use the local statistics within a neighborhood to estimate the centered noisy pixel, and tend to damage image details due to the image local diversity singularity and non-stationarity. To address this problem, in this paper, iterative nonlocal means filter (INLM) is proposed to exploit the image non-local similarity feature in the S&P noise removal procedure. Moreover, the proposed iterative framework update the similarity weights and the estimated values for higher accuracy. The experimental results show that the proposed INLM produces better results than state-of-art methods over a wide range of scenes both subjectively and objectively, and it is robust to the detection results.     

An effective 2-stage method for removing impulse noise in images

In this paper, a robust 2-stage impulse noise removal system is proposed to remove impulse noise from extremely corrupted images. The contributions are in two-fold. First, a neuro-fuzzy based impulse noise detector (NFIDET) is introduced to identify the noisy pixels. NFIDET is a powerful noise detector that can handle image corruption even up to 90% with zero miss and false detection rate with a simple neuro-fuzzy structure. This is the best result among the other impulse noise detectors in the literature. Second, this paper presents a new approach for weight calculation of adaptive weighted mean filter by using robust statistical model. An adaptive robust weighted mean (ARWM) filter removes a detected noisy pixel by adaptively determining filtering window size and replacing a noisy pixel with the weighted mean of the noise-free pixels in its window. A Geman–McClure robust estimation function is used to estimate the weights of the pixels. Simulation results also show that the proposed robust filter substantially outperforms many other existing algorithms in terms of image restoration.     

Detail preserving impulsive noise removal

Most image processing applications require noise elimination. For example, in applications where derivative operators are applied, any noise in the image can result in serious errors. Impulsive noise appears as a sprinkle of dark and bright spots. Transmission errors, corrupted pixel elements in the camera sensors, or faulty memory locations can cause impulsive noise. Linear filters fail to suppress impulsive noise. Thus, non-linear filters have been proposed. Windyga's peak-and-valley filter, introduced to remove impulsive noise, identifies noisy pixels and then replaces their values with the minimum or maximum value of their neighbors depending on the noise (dark or bright). Its main disadvantage is that it removes fine image details. In this work, a variation of the peak-and-valley filter is proposed to overcome this problem. It is based on a recursive minimum–maximum method, which replaces the noisy pixel with a value based on neighborhood information. This method preserves constant and edge areas even under high impulsive noise probability. Finally, a comparison study of the peak-and-valley filter, the median filter, and the proposed filter is carried-out using different types of images. The proposed filter outperforms other filters in the noise reduction and the image details preservation. However, it operates slightly slower than the peak-and-valley filter.     

双迭代等距均值滤波的医学图像恢复北大核心CSCD

医学图像中往往有很多与脉冲噪声灰度相同的像素,因此含脉冲噪声的医学图像的恢复非常困难。为了获得比现有的脉冲噪声滤波器更好的噪声抑制和纹理结构保持效果,提出了一种双迭代等距均值滤波(dual iterative equidistant mean filter,DIEMF)的医学图像恢复方法。该方法采用等距离邻域进行噪声检测和去除;噪声检测器循环地利用邻域的非最值像素与中心像素之间的平均绝对差,以及利用多数原则,将噪声像素与无噪像素区分开来;噪声去除采用自适应和双迭代的方法,以等距邻域中无噪像素和先前恢复像素的平均值作为中心噪声像素的灰度估计值,充分利用最近的先前恢复的像素。实验结果表明,该方法在噪声抑制和纹理结构保持方面优于现有的方法,特别是对于低密度噪声,它比现有的滤波器具有显著的优越性。     

基于差分法的噪声检测和变分法的图像滤波算法

针对图像的椒盐噪声滤除算法中,在噪声检测阶段对噪声点的检测通常不够准确,在噪声恢复阶段,又缺乏对边缘信息的保护,文中提出了一种两步复原法,以用于复原被脉冲噪声破坏的模糊图像。算法将滤噪过程分为噪声检测和噪声恢复阶段。噪声检测过程中,在滑动窗口扩大当前的像素值和其他像素值之间的有序差异,来确定当前像素是否为噪声像素。而在噪声恢复过程中利用变分法,确保图像的边缘和细节。实验结果表明,文中所提检测、降噪方法在噪声密度较高的情况下,优于其他算法。     

Recursive cubic spline interpolation filter approach for the removal of high density salt-and-pepper noise

Spline-based approach is proposed to remove very high density salt-and-pepper noise in grayscale and color images. The algorithm consists of two stages, the first stage detects whether the pixel is noisy or noise-free. The second stage removes the noisy pixel by recursive spline interpolation filter. The proposed recursive spline interpolation filter is based on the neighborhood noise-free pixels and previous noise-free output pixel; hence, it is termed as recursive spline interpolation filter. The performance of the proposed algorithm is compared with the existing algorithms like standard median filter, decision-based filter, progressive switched median filter, and modified decision-based unsymmetric trimmed median filter at very high noise density. The proposed algorithm gives better peak signal-to-noise ratio, image enhancement factor, and correlation factor results than the existing algorithms.     

Curvelet based nonlocal means algorithm for image denoising

In this work, a curvelet based nonlocal means denoising method is proposed. In the proposed method, the curvelet transform is firstly implemented on the noisy image to produce reconstructed images. Then the similarity of two pixels in the noisy image is computed based on these reconstructed images which include complementary image features at relatively high noise levels or both the reconstructed images and the noisy image at relatively low noise levels. Finally, the pixel similarity and the noisy image are utilized to obtain the final denoised result using the nonlocal means method. Quantitative and visual comparisons demonstrate that the proposed method outperforms the state-of-art nonlocal means denoising methods in terms of noise removal and detail preservation.     

基于极值检测的图像滤波算法

针对极值中值滤波法在去除椒盐噪声时存在的不足,提出了一种改进的图像滤波算法.首先使用极值法检测图像中的噪声点,然后采用窗口由小到大变化的自适应算法得到噪声像素的滤波值,最后通过计算噪声像素滤波前后灰度值的差值来修正被误判像素的灰度值.对不同类型、受不同强度噪声污染图像的去噪实验表明,该方法在不同噪声率下均优于传统的中值滤波法及其一些改进算法,当噪声率较大时其去噪和保边性能得到了显著提高.     

快速高效去除图像椒盐噪声的均值滤波算法

针对常见滤除椒盐噪声算法需要使用阈值、运算时间长、去除噪声效果不理想等缺陷,提出了一种快速高效去除图像椒盐噪声的均值滤波算法。新算法对滤波窗口下的疑似噪声像素,有针对性地选择少数信号像素构成信号像素集合,取集合中的元素均值对疑似噪声像素进行滤波。实验结果表明,对于噪声密度为1%到99%的图像,新算法均具有良好的去除噪声能力和保持细节能力,而且整个算法耗费时间很少,因而具有较大的实用性。     

Combining spatial and scale-space techniques for edge detection to provide a spatially adaptive wavelet-based noise filtering algorithm

New methods for detecting edges in an image using spatial and scale-space domains are proposed. A priori knowledge about geometrical characteristics of edges is used to assign a probability factor to the chance of any pixel being on an edge. An improved double thresholding technique is introduced for spatial domain filtering. Probabilities that pixels belong to a given edge are assigned based on pixel similarity across gradient amplitudes, gradient phases and edge connectivity. The scale-space approach uses dynamic range compression to allow wavelet correlation over a wider range of scales. A probabilistic formulation is used to combine the results obtained from filtering in each domain to provide a final edge probability image which has the advantages of both spatial and scale-space domain methods. Decomposing this edge probability image with the same wavelet as the original image permits the generation of adaptive filters that can recognize the characteristics of the edges in all wavelet detail and approximation images regardless of scale. These matched filters permit significant reduction in image noise without contributing to edge distortion. The spatially adaptive wavelet noise-filtering algorithm is qualitatively and quantitatively compared to a frequency domain and two wavelet based noise suppression algorithms using both natural and computer generated noisy images.     

基于噪声分离和小波阈值自适应图像去噪算法

针对VisuShrink小波阈值滤波算法的不足和混合噪声的情况,提出了一种基于噪声分离和尺度的自适应混合图像去噪算法.算法首先通过极值检测分离脉冲噪声和高斯噪声,然后分别对脉冲噪声应用多窗口中值滤波及高斯噪声应用基于尺度的小波阈值滤波完成去噪.实验表明,该混合滤波算法能有效去除图像中的脉冲噪声和高斯噪声,并较好地保存了...     

Erratum to: Multi-scale directional-filtering-based method for follicular lymphoma grading

A new decision-based algorithm has been proposed for the restoration of digital images which are highly contaminated by the saturated impulse noise (i.e., salt-and-pepper noise). The proposed denoising algorithm performs filtering operation only to the corrupted pixels in the image, keeping uncorrupted pixels intact. The present study has used a coupled window scheme for the removal of high density noise. It has used sliding window of increasing dimension, centered at any pixel and replaced the noisy pixels consecutively by the median value of the window. However, if the entire pixels in the window are noisy, then the dimension of sliding window is increased in order to obtain the noise-free pixels for median calculation. Consequently, this algorithm has been found to be able to remove the high density salt-and-pepper noise and also preserved the fine details of the four images, Lena, Elaine, Rhythm, and Sunny, used as test images in this study (The latter two real-life images have been acquired using Sony: Steady Shot DSC- S3000). Experimentally, it has been found that the proposed algorithm yields better peak signal-to-noise ratio, image enhancement factor, structural similarity index measure and image quality index, compared with the other state-of-art median-based filters viz. standard median filter, adaptive median filter, progressive switched median filter, modified decision-based algorithm and modified decision-based unsymmetric trimmed median filter.     

Multiresolution Bilateral Filtering for Image Denoising

The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges; it has shown to be an effective image denoising technique. An important issue with the application of the bilateral filter is the selection of the filter parameters, which affect the results significantly. There are two main contributions of this paper. The first contribution is an empirical study of the optimal bilateral filter parameter selection in image denoising applications. The second contribution is an extension of the bilateral filter: multiresolution bilateral filter, where bilateral filtering is applied to the approximation (low-frequency) subbands of a signal decomposed using a wavelet filter bank. The multiresolution bilateral filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. Experimental results with both simulated and real data are provided.      

Impulsive noise detection by second-order differential image and noise removal using adaptive nearest neighbourhood filter

A novel impulsive noise detection method based on the principle that the difference between the noisy pixel and the nearest good pixel will be different from the difference between two nearby good pixels. This is achieved by constructing a second-order differential image. Three new noise removal methods are presented. Simulated results show that the proposed filter gives far better results than many existing filters and is comparable to the results obtained by JM filter based on Jarque-Bera test. Our noise detection method is computationally simpler.     

An improved recursive and adaptive median filter for high density impulse noise

An improved recursive and adaptive median filter (RAMF) for the restoration of images corrupted with high density impulse noise is proposed in the present paper. Adaptive operation of the filter is justified with the variation in size of working window which is centered at noisy pixels. Based on the presence of noise-free pixel(s), the size of working window changes. The noisy pixels are filtered through the replacement of their values using both noise-free pixels of the current working window and previously processed noisy pixels of that window. These processed noisy pixels are obtained recursively. The combined effort thus provides an improved platform for filtering high density impulse noise of images. Experimental results with several real-time noisy images show that the proposed RAMF outperforms other state-of-the-art filters quantitatively in terms of peak signal to noise ratio (PSNR) and image enhancement factor (IEF). The superiority of the filter is also justified qualitatively through visual interpretation.     

一种自适应多尺度积阈值的图像去噪算法

该文提出了平稳小波变换(Stationary Wavelet Transform, SWT )域自适应多尺度积阈值的图像去噪算法(SWT domain Multiscale Products, SWTMP)。与传统的阈值去噪算法不同,该阈值不是直接作用于小波系数,而是作用于小波系数的空间多尺度积。分析了SWT域含噪图像多尺度积的特点,提出了SWT域自适应多尺度积阈值的计算方法。多尺度积强化了图像的重要结构信息,弱化了噪声,在有效去噪的同时更多地保留了图像的边缘和细节。实验结果表明,所提算法对自然图像去噪后的视觉效果和性能指标均好于二进小波域多尺度积阈值(Adaptive Multiscale Products Thresholding, AMPT)去噪方法。     

High performance detection filter for impulse noise removal in images

A high performance detection (HPD) filter is proposed for impulse noise removal in images. In this approach, the noisy pixels are detected iteratively through several phases, based on a set of unique similarity criteria. Simulation results show that the HPD filter outperforms others at medium to high noise rates and suppresses impulse noise effectively while preserving image details, even thin lines.     

Image Restoration by Lifting‐Based Wavelet Domain E‐Median Filter

In this paper, we propose a method of applying a lifting‐based wavelet domain e‐median filter (LBWDEMF) for image restoration. LBWDEMF helps in reducing the number of computations. An e‐median filter is a type of modified median filter that processes each pixel of the output of a standard median filter in a binary manner, keeping the output of the median filter unchanged or replacing it with the original pixel value. Binary decision‐making is controlled by comparing the absolute difference of the median filter output and the original image to a preset threshold. In addition, the advantage of LBWDEMF is that probabilities of encountering root images are spread over sub‐band images, and therefore the e‐median filter is unlikely to encounter root images at an early stage of iterations and generates a better result as iteration increases. The proposed method transforms an image into the wavelet domain using lifting‐based wavelet filters, then applies an e‐median filter in the wavelet domain, transforms the result into the spatial domain, and finally goes through one spatial domain e‐median filter to produce the final restored image. Moreover, in order to validate the effectiveness of the proposed method we compare the result obtained using the proposed method to those using a spatial domain median filter (SDMF), spatial domain e‐median filter (SDEMF), and wavelet thresholding method. Experimental results show that the proposed method is superior to SDMF, SDEMF, and wavelet thresholding in terms of image restoration.